The present invention relates to a fuel supply control system for an internal combustion engine, and more particularly to a fuel supply control system in which an intake air flow rate of the internal combustion engine is detected by an intake air flow rate sensor, and an amount of fuel to be supplied to the engine is controlled according to the detected intake air flow rate.
A method of detecting an intake air flow rate of the internal combustion engine with a hotwire flow meter is conventionally known. The characteristic of the hotwire flow meter changes due to aging. Therefore, there is a problem of a detection error of the intake air flow rate increasing, if the hotwire flow meter is being used for a long time. To cope with this problem, a method of calculating a learning correction value according to changes in the characteristic of the hotwire flow meter is shown in Japanese Patent Laid-Open (Kokoku) Hei 7-23702.
According to this method, an air-fuel ratio negative feedback amount CFB is calculated according to an output of an air-fuel ratio sensor provided in an exhaust system of the internal combustion engine, so that the detected air-fuel ratio coincides with a target value. Further, a plurality of values CL1, CL2, and CL3 of the air-fuel ratio negative feedback amount CFB, which correspond respectively to a plurality of flow rate points QL1, QL2, and QL3, representative of the characteristic change in the hotwire flow meter, are stored in a memory. The learning correction value is calculated by means of the interpolation or extrapolation according to the data stored in the memory and the intake air flow rate Q detected by the hotwire flow meter.
In the method shown in Japanese Patent Laid-Open (Kokoku) Hei 7-23702, the values CL1, CL2, and CL3 of the air-fuel ratio negative feedback amount CFB corresponding to the predetermined flow rate points QL1, QL2, and QL3 are stored in the memory, and the stored data are used for calculation of the learning correction value. Accordingly, if the values CL1, CL2, and CL3 of the air-fuel ratio negative feedback amount CFB in the memory change due to a change in the engine operating condition, the learning correction value directly reflects the changes in the values CL1, CL2, and CL3, which results in a large variation in the learning correction value. In addition, according to this method, the characteristic change in the hotwire flow meter is monitored in the plurality of flow rate points QL1, QL2, and QL3. When increasing the number of the monitoring points in order to improve accuracy of the learning correction value, the memory capacity increases. Accordingly, from the view point of manufacturing costs, it is not preferable to greatly increase the number of the monitoring points.
The recent tightening of emission regulations (harmful gas emission) has highlighted that the deterioration or the characteristic change in parts of the engine or the engine control devices, causes an adverse effect on the exhaust characteristics of the engine. Therefore, it is desirable to obtain the learning correction coefficient with a higher degree of accuracy depending on the characteristic change in the intake air flow rate sensor.
A method of determining an abnormality or a deterioration in the intake air flow rate sensor is known from Japanese Patent Laid-Open (Kokoku) Hei 8-6623. In this method, the abnormality or the deterioration is detected based on the detected values of the air-fuel ratio sensor, the throttle valve opening sensor, and the engine rotational speed sensor.
According to this determining method of the characteristic deterioration (abnormality) of the intake air flow rate sensor, the determination is performed not with the statistically processed data of the sensor detected values, but with the sensor detected values themselves. Therefore, there is a problem of the determination accuracy becoming lower, when the frequency of the determination is increased.
A first object of the present invention is to provide a fuel supply control system for an internal combustion engine, which can obtain an accurate learning correction value that compensates for an influence of the characteristic change in the intake air flow rate sensor, to thereby maintain good controllability of the air-fuel ratio control.
A second object of the present invention is to provide a fuel supply control system for an internal combustion engine, which can regularly monitor an operation of the intake air flow rate sensor to accurately determine an abnormality in the intake air flow rate sensor.
To achieve the first object, the present invention provides a fuel supply control system for an internal combustion engine, including intake air flow rate detecting means, basic fuel amount calculating means, an air-fuel ratio sensor provided in an exhaust system of the engine, air-fuel ratio correction coefficient calculating means, correlation parameter calculating means, learning means, and fuel amount control means. The intake air flow rate detecting means detects an intake air flow rate (QAIR) of the engine. The basic fuel amount calculating means calculates a basic fuel amount (TIM) supplied to the engine, according to the intake air flow rate (QAIR) detected by the intake air flow rate detecting means. The air-fuel ratio correction coefficient calculating means calculates an air-fuel ratio correction coefficient (KAF) for correcting an amount of fuel to be supplied to the engine so that the air-fuel ratio detected by the air-fuel ratio sensor coincides with a target air-fuel ratio. The correlation parameter calculating means calculates at least one correlation parameter vector (xcex81, xcex82) which defines a correlation between the air-fuel ratio correction coefficient (KAF) and the intake air flow rate (QAIR) detected by the intake air flow rate detecting means, using a sequential statistical processing algorithm. The learning means calculates a learning correction coefficient (KREFG) relating to a change in characteristics of the intake air flow rate detecting means, using the at least one correlation parameter vector (xcex81, xcex82). The fuel amount control means controls an amount (TOUT) of fuel to be supplied to the engine, using the basic fuel amount (TIM), the air-fuel ratio correction coefficient (KAF), and the learning correction coefficient (KREFG).
With this configuration, at least one correlation parameter vector which defines a correlation between the air-fuel correction coefficient, which corrects an amount of fuel supplied to the engine so that the air-fuel ratio coincides with the target air-fuel ratio, and the intake air flow rate detected by the intake air flow rate detecting means, can be calculated using the sequential statistical processing algorithm. Further, the learning correction coefficient relating to a change in characteristics of the intake air flow rate detecting means can be calculated using the at least one correlation parameter vector. The amount of fuel to be supplied to the engine is controlled using the air-fuel ratio correction coefficient, the learning correction coefficient, and the basic fuel amount, which can be set according to the intake air flow rate detected by the intake air flow rate detecting means. That is, at least one correlation parameter vector is calculated with the statistical processing based on many detected data, and the learning correction coefficient is calculated using the calculated correlation parameter vector. Therefore, it is possible to obtain the learning correction coefficient with a high degree of accuracy that corresponds to an averaged state of the ever-changing engine operating conditions. In addition, since the sequential statistical processing algorithm is used, no special computing device such as a CPU is required for statistical processing, and the computation for the statistical processing can be executed with a relatively small memory capacity.
Preferably, the fuel supply control system further includes abnormality determining means for determining an abnormality in the intake air flow rate detecting means according to an element (A1, A2) of the at least one correlation parameter vector (xcex81, xcex82).
With this configuration, the abnormality in the intake air flow rate detecting means can be determined according to the element of the at least one correlation parameter vector. Accordingly, the operation of the intake air flow rate detecting means is regularly monitored to increase frequency of the abnormality determination and improve accuracy of the abnormality determination.
Preferably, the correlation parameter calculating means calculates a plurality of correlation parameter vectors (xcex81, xcex82) corresponding to a plurality of operating regions (R1, R2) of the engine.
With this configuration, a high degree of accuracy of the learning correction coefficient can be maintained over a wide range of the engine operating conditions.
Preferably, the correlation parameter calculating means calculates a plurality of correlation parameter vectors (xcex81, xcex82), each of which defines the correlation with a linear expression, and the learning means switches the correlation parameter vector (xcex81, xcex82) that is used for calculating the learning correction coefficient (KREFG), at an intersection (PX) of straight lines (LR1, LR2) corresponding to the linear expressions.
With this configuration, the correlation parameter vector that is used for calculating the learning correction coefficient can be switched at an intersection of the straight lines corresponding to a plurality of the correlation parameter vector. Accordingly, the learning correction coefficient is prevented from abruptly changing when the correlation parameter vector is switched, which then results in a smooth switching.
Preferably, the correlation parameter calculating means calculates the correlation parameter vector (xcex81, xcex82), when the engine is operating in a predetermined operating condition.
With this configuration, the correlation parameter vector is calculated when the engine is operating in the predetermined operating condition. Accordingly, the correlation parameter vector is accurately calculated which improves accuracy of the learning correction.
Preferably, the correlation parameter calculating means calculates a modified air-fuel ratio correction coefficient (KAFMOD) by modifying the air-fuel ratio correction coefficient (KAF) with the learning correction coefficient (KREFG), and calculates the correlation parameter vector (xcex81, xcex82), using the modified air-fuel ratio correction coefficient (KAFMOD).
With this configuration, the air-fuel ratio correction coefficient can be modified by the learning correction coefficient to thereby calculate the modified air-fuel ratio correction coefficient. Then, the correlation parameter vector can be calculated using the modified air-fuel ratio correction coefficient instead of the air-fuel ratio correction coefficient. If the air-fuel ratio correction coefficient itself is used, there is a possibility that the learning control by the learning correction coefficient may result in a hunting condition. The hunting condition is an attempt to establish the learning correction coefficient in order to calculate the correlation parameter vector. Such a problem can be avoided by using the modified air-fuel ratio correction coefficient.
Preferably, the correlation parameter calculating means calculates the correlation parameter vector (xcex81, xcex82), using a deviation (KAF-1) between the air-fuel ratio correction coefficient (KAF) and a central value of the air-fuel ratio correction coefficient.
With this configuration, the deviation between the air-fuel ratio correction coefficient and a central value of the air-fuel ratio correction coefficient is used instead of only the air-fuel ratio correction, coefficient to calculate the correlation parameter vector. The deviation varies around zero which is the center of the variation range. Accordingly, the correlation parameter vector can be obtained with a higher degree of accuracy, when using the sequential statistical processing algorithm.
Preferably, the correlation parameter calculating means uses the sequential statistical processing algorithm, limiting values of elements (A1, B1, A2, B2) of the correlation parameter vector (xcex81, xcex82) within a predetermined range. Accordingly, a stable correlation parameter vector can be obtained.
To achieve the second object, the present invention provides a fuel supply control system for an internal combustion engine, including intake air flow rate detecting means, basic fuel amount calculating means, an air-fuel ratio sensor provided in an exhaust system of the engine, air-fuel ratio correction coefficient calculating means, correlation parameter calculating means, fuel amount control means, and abnormality determining means. The intake air flow rate detecting means detects an intake air flow rate (QAIR) of the engine. The basic fuel amount calculating means calculates a basic fuel amount (TIM) supplied to the engine, according to the intake air flow rate (QAIR) detected by the intake air flow rate detecting means. The air-fuel ratio correction coefficient calculating means calculates an air-fuel ratio correction coefficient (KAF) for correcting an amount of fuel to be supplied to the engine so that the air-fuel ratio detected by the air-fuel ratio sensor coincides with a target air-fuel ratio. The correlation parameter calculating means calculates at least one correlation parameter vector (xcex81, xcex82) which defines a correlation between the air-fuel ratio correction coefficient (KAF) and the intake air flow rate (QAIR) detected by the intake air flow rate detecting means, using a sequential statistical processing algorithm. The fuel amount control means controls an amount (TOUT) of fuel to be supplied to the engine, using the basic fuel amount (TIM) and the air-fuel ratio correction coefficient (KAF). The abnormality determining means determines an abnormality in the intake air flow rate detecting means according to an element (A1, A2) of the at least one correlation parameter vector (xcex81, xcex82).
With this configuration, at least one correlation parameter vector is calculated using the sequential statistical processing algorithm. The correlation parameter defines a correlation between the air-fuel correction coefficient, which corrects an amount of fuel supplied to the engine so that the air-fuel ratio coincides with the target air-fuel ratio, and the intake air flow rate detected by the intake air flow rate detecting means. The amount of fuel to be supplied to the engine is controlled using the air-fuel ratio correction coefficient and the basic fuel amount which is set according to the intake air flow rate detected by the intake air flow rate detecting means. Further, an abnormality in the intake air flow rate detecting means can be determined according to the element of the at least one correlation parameter vector. As a result, the operation of the intake air flow rate detecting means is regularly monitored to improve accuracy of the abnormality determination.